Unlocking AI Potential: Snowflake ML Jobs Reaches General Availability

On August 12, 2025, Snowflake announced the general availability (GA) of ML Jobs, marking a significant milestone for enterprises looking to accelerate AI adoption without the headaches of managing complex machine learning (ML) infrastructure. This release empowers data scientists, analysts, and engineers to run ML workflows directly within Snowflake’s secure, governed environment.

In this tutorial-style post, we’ll explore how ML Jobs simplifies ML workflows, walk through a practical implementation, and highlight use cases across industries. If you’ve been relying on external ML platforms, you’ll quickly see why Snowflake ML Jobs is a game-changer.

The Real-World Challenge: External ML Complexity

Picture a financial services data team tasked with building fraud detection models. Historically, their workflow might look like this:

1. Exporting Data from Snowflake to external ML platforms.
2. Preprocessing in one environment, often duplicating steps.
3. Training Models with inconsistent governance and security.
4. Reimporting Results back into Snowflake for downstream analytics.

This approach introduces delays, security risks, and extra infrastructure costs. With ML Jobs, those hurdles vanish—because the entire ML lifecycle runs inside Snowflake.

What Are Snowflake ML Jobs?

Snowflake ML Jobs allow users to define, schedule, and execute ML workflows natively in Snowflake using SQL and Python. Whether training models, running inference, or managing pipelines, ML Jobs make the process seamless.

Key features include:

• Native Integration with Cortex AI: Use prebuilt LLMs and custom ML models without leaving Snowflake.
• Workflow Management: Train, validate, and deploy models as scheduled jobs.
• SQL-First Approach: Lower barrier of entry for analysts who already know SQL.
• Governance and Security: Data never leaves Snowflake’s environment.

Technical Deep Dive: How ML Jobs Work

At its core, ML Jobs combine Snowflake’s scheduling capabilities with machine learning runtimes. You can:

• Define training and inference tasks using SQL or Python UDFs.
• Schedule recurring jobs for retraining or batch predictions.
• Leverage Cortex AI for embeddings, natural language, or classification tasks.

Example: Training a Model with ML Jobs

Let’s walk through a step-by-step workflow.

Step 1: Prepare Data

CREATE OR REPLACE TABLE transactions_train AS
SELECT amount, location, time, label
FROM raw_transactions
WHERE date < '2025-07-01';

Step 2: Train a Model

CREATE OR REPLACE SNOWFLAKE.ML_JOB fraud_detection_train
USING (
  SELECT * FROM transactions_train
)
OPTIONS (
  task = 'train',
  target_column = 'label',
  model_type = 'logistic_regression'
);

Step 3: Run Inference

CREATE OR REPLACE TABLE fraud_predictions AS
SELECT transaction_id,
       PREDICT(fraud_detection_train, amount, location, time) AS prediction
FROM new_transactions;

This simple flow demonstrates how teams can handle end-to-end ML within Snowflake, with no external orchestration required.

Benefits of Snowflake ML Jobs

1. Reduced Infrastructure Costs
   No need for separate ML servers or pipelines. Everything runs where the data resides.
2. Faster Time-to-Value
   By eliminating data movement, ML models can be trained and deployed faster.
3. Governance and Compliance
   Sensitive data stays within Snowflake, ensuring compliance with regulations like GDPR and HIPAA.
4. Collaboration Across Teams
   SQL analysts, data scientists, and engineers can work in the same environment without friction.

Integration with Cortex AI

Snowflake ML Jobs pair seamlessly with Cortex AI, the company’s AI framework for running LLMs and advanced models.

• Vector Search + ML Jobs: Build semantic search systems.
• LLM Integration: Fine-tune models and schedule retraining jobs.
• Automated Pipelines: Combine Cortex-powered embeddings with ML Jobs for production-grade applications.

This integration allows teams to go beyond traditional ML, enabling AI-native applications.

Use Cases Across Industries

Finance

• Fraud detection
• Credit risk scoring

Retail

• Personalized product recommendations
• Demand forecasting

Healthcare

• Patient outcome prediction
• Drug discovery pipelines

Manufacturing

• Predictive maintenance
• Quality assurance models

Each industry benefits from ML Jobs’ native workflow orchestration and secure environment.

Migration Tips for Existing ML Workflows

If you’re already running ML pipelines outside Snowflake, here’s how to migrate smoothly:

1. Audit Existing Workflows: Identify which models rely heavily on Snowflake data.
2. Rebuild Preprocessing in SQL: Replace external preprocessing scripts with SQL transformations.
3. Port Training Tasks: Use ML Jobs’ task=train functionality to replicate external jobs.
4. Leverage Cortex for LLM Needs: Offload embedding and NLP tasks to Cortex AI.
5. Gradually Decommission External Tools: As workflows stabilize, cut infrastructure costs by retiring redundant systems.

Conclusion: A New Era for Data Scientists

The general availability of Snowflake ML Jobs is more than just a feature release—it’s a paradigm shift. By running ML directly where the data lives, Snowflake eliminates complexity, reduces costs, and accelerates AI adoption across industries.

For data scientists, this means:

• Less time managing infrastructure.
• More time building impactful models.
• A unified environment for data, ML, and AI.

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